About the job
Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.
Responsibilities
Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability
Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure
Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance
Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams
Build and maintain production logging, monitoring dashboards, and evaluation infrastructure
Add new capabilities to the training codebase, such as long context support or novel architectures
Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams
Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned
Qualifications
Minimum
Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other
Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure
Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs
Excel at debugging complex, ambiguous problems across multiple layers of the stack
Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents
Are passionate about the work itself and want to refine your craft as a research engineer
Care about the societal impacts of AI and responsible scaling
Preferred
Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
Contributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)
Published research on model training, scaling laws, or ML systems
Experience with production ML systems, observability tools, or evaluation infrastructure
Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence